AI RESEARCH
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
arXiv CS.LG
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ArXi:2302.08724v4 Announce Type: replace-cross Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of increased computation due to its incompatibility to subsampling of the likelihood. New Piecewise Deterministic Marko Process (PDMP) samplers permit subsampling, though